The present invention relates to product checkout devices and more specifically to a produce recognition system and method.
Bar code readers are well known for their usefulness in retail checkout and inventory control. Bar code readers are capable of identifying and recording most items during a typical transaction since most items are labeled with bar codes.
Items which are typically not identified and recorded by a bar code reader are produce items, since produce items are typically not labeled with bar codes. Bar code readers may include a scale for weighing produce items to assist in determining the price of such items. But identification of produce items is still a task for the checkout operator, who must identify a produce item and then manually enter an item identification code. Operator identification methods are slow and inefficient because they typically involve a visual comparison of a produce item with pictures of produce items, or a lookup of text in table. Operator identification methods are also prone to error, on the order of fifteen percent.
Therefore, it would be desirable to provide a produce recognition system and method. It would also be desirable to provide a produce data collector with a reference apparatus that makes calibration easier.
In accordance with the teachings of the present invention, a produce recognition system and method are provided.
The produce recognition system includes a produce data collector and a computer. The produce data collector collects first data from an external reference, collects second and third data from an internal reference, and collects fourth data from a produce item. A computer determines a first calibration value from the first and second data and a second calibration value from the third data and applies the first and second calibration values to the fourth data to produce fifth data. The computer further obtains sixth data from reference produce data and compares the fifth and sixth data to identify the produce item.
A method of identifying a produce item includes the steps of obtaining calibration information for a produce data collector, collecting first data describing the produce item by the produce data collector, applying the calibration information to the first data to produce second data, obtaining a number of previously stored third data associated with a plurality of produce items, comparing the second data to the third data to determine fourth data and a corresponding produce item from the third data which is most like the second data, and identifying the produce item to be the corresponding produce item.
A method of calibrating produce data collected by a produce data collector includes the steps of obtaining a first calibration value for the produce data collector using an external reference and an internal reference, obtaining a second calibration value for the produce data collector using only the internal reference, and applying the first and second calibration values to the produce data.
It is accordingly an object of the present invention to provide a produce recognition system and method.
It is another object of the present invention to provide a produce recognition system and method which identifies produce items by comparing their spectral data with those in a spectral data library.
It is another object of the present invention to provide the produce data collector with a reference apparatus that makes calibration easier.
It is another object of the present invention to provide the produce data collector with an internal reference for automatic calibration.
It is another object of the present invention to provide a produce data collector which uses an internal reference for indirect inter-device calibration.
It is another object of the present invention to provide an indirect inter-device calibration method for a produce data collector.